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An Overview of Parameter and Data Strategies for K-Nearest Neighbours Based Short-Term Traffic Prediction
Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.ORCID iD: 0000-0001-5824-425X
Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
2017 (English)In: Proceedings of International Conference on Intelligent Traffic and Transportation (ICITT), 2017, Vol. 09Conference paper, Published paper (Refereed)
Abstract [en]

Modern intelligent transportation systems (ITS) requires reliable and accurate short-term traffic prediction. One widely used method to predict traffic is k-nearest neighbours (kNN). Though many studies have tried to improve kNN with parameter strategies and data strategies, there is no comprehensive analysis of those strategies. This paper aims to analyse kNN strategies and guide future work to select the right strategy to improve prediction accuracy. Firstly, we examine the relations among three kNN parameters, which are: number of nearest neighbours (k), search step length (d) and window size (v). We also analysed predict step ahead (m) which is not a parameter but a user requirement and configuration. The analyses indicate that the relations among parameters are compound especially when traffic flow states are considered. The results show that strategy of using v leads to outstanding accuracy improvement. Later, we compare different data strategies such as flow-aware and time-aware ones together with ensemble strategies. The experiments show that the flowaware strategy performs better than the time-aware one. Thus, we suggest considering all parameter strategies simultaneously as ensemble strategies especially by including v in flow-aware strategies.

Place, publisher, year, edition, pages
2017. Vol. 09
Keyword [en]
Short-Term Traffic Prediction, k-Nearest Neighbours Regression, Parameter and Data Strategies
National Category
Computer Science Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:bth-15210OAI: oai:DiVA.org:bth-15210DiVA: diva2:1145423
Conference
International Conference on Intelligent Traffic and Transportation (ICITT), Zurich
Available from: 2017-09-28 Created: 2017-09-28 Last updated: 2017-10-02Bibliographically approved

Open Access in DiVA

The full text will be freely available from 2018-06-01 08:32
Available from 2018-06-01 08:32

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Sun, BinWei, ChengPrashant, GoswamiGuohua, Bai
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf